Automated assessment of breast cancer margin in optical coherence tomography images via pre‐trained convolutional neural network
The benchmark method for the evaluation of breast cancers involves microscopic testing of a hematoxylin and eosin (H&E) stained tissue biopsy. Resurgery is required in 20‐30% of cases because of incomplete excision of malignant tissues. Therefore, a more accurate method is required to detect the cancer margin to avoid the risk of recurrence. In the recent years, convolutional neural networks (CNNs) has achieved excellent performance in the field of medical images diagnosis. It automatically extracts the features from the images and classifies them. In the proposed study, we apply a pre‐trained Inception‐v3 CNN with reverse active ...
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UCLA
